On Tue, Sep 21, 2010 at 2:29 PM, Sturla Molden <sturla@molden.no> wrote:
>> Hi,
>>>> Does Scipy contain the ability to fit a sigmoid curve to a set of data
>> points?
>>>> I found some Numpy code
>> (http://pingswept.org/2009/01/24/least-squares-polynomial-fitting-in-python/)
>> for fitting curves using the least squares method, but it only seems
>> to fit parabolas to my sigmoid data.
>> scipy.optimize.leastsq can fit any non-linear regression curve using
> Levenberg-Marquardt. You will have to supply a function that computes the
> residuals, and optionally a function that returns their Jacobian unless
> you want it estimated.
>> Sturla
>Ignoring that you data is the opposite, there are a lot of 'growth
curves' such as Richard's (growth) curve or generalized logistic
function.
http://en.wikipedia.org/wiki/Generalised_logistic_function
Also, you can fit a linear model if your model can be linearized. The
downside is that the errors are multiplicative rather than additive.
For nonlinear models, it usually helps to standardize your data.
Bruce